store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_118596', 'seed': 118596, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[23060, 30293, 10719, 50928, 5258, 11257, 44397, 49329, 45685, 44499, 24516, 40993, 34343, 8376, 35580, 13561, 50873, 3254, 2434, 36847]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6208, 'nll': 2.6811133289337157}, 'chosen_samples': [20233, 43678, 6171, 22835, 14333, 23344, 10650, 20242, 14394, 52808, 32142, 35051, 53037, 32070, 58503, 55088, 27139, 12131, 22647, 46367, 21495, 24433, 36492, 26785, 59141, 32940, 46161, 39440, 1245, 8665, 35797, 36150, 31037, 43086, 236, 37087, 27060, 12297, 35441, 1841], 'chosen_samples_score': ['1.0716815', '1.071979', '1.0730877', '1.0760989', '1.0751934', '1.0797741', '1.0746782', '1.0798788', '1.0802424', '1.0782919', '1.0743282', '1.0811229', '1.0899987', '1.0815535', '1.0831625', '1.0828934', '1.1036096', '1.082825', '1.1009002', '1.0852871', '1.0839183', '1.08512', '1.1051369', '1.1059979', '1.1243019', '1.1321819', '1.1059546', '1.1231179', '1.1054163', '1.119189', '1.1263499', '1.138108', '1.1465216', '1.158572', '1.1394181', '1.1684105', '1.1556959', '1.1944394', '1.1467947', '1.1647518']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7775, 'nll': 1.1285737707138062}, 'chosen_samples': [28116, 35449, 8488, 23279, 34475, 53797, 8958, 11668, 24541, 51231, 5166, 22511, 24010, 8552, 53344, 26956, 24310, 28714, 59433, 10471, 38404, 22981, 8021, 1195, 45810, 47115, 1552, 4767, 51761, 44030, 39256, 14635, 56174, 138, 31000, 51544, 59418, 40549, 10045, 5393], 'chosen_samples_score': ['0.87307817', '0.90561664', '0.93763155', '0.9137913', '0.95072013', '0.8809728', '0.9313609', '0.9501073', '0.87736243', '0.8924322', '1.0505128', '0.88071764', '0.963748', '0.8843191', '1.0594196', '0.90361255', '0.898178', '0.874817', '0.8805388', '0.88676655', '0.9670288', '0.8782361', '0.8734016', '0.9303617', '0.89405507', '0.87809604', '0.89625776', '0.88090706', '0.8832426', '0.9538658', '0.91287607', '0.8891816', '0.87472', '0.88101685', '0.8764663', '0.87430626', '0.9295843', '0.93774384', '0.95345384', '0.9424009']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8264, 'nll': 0.9976074645996094}, 'chosen_samples': [34252, 26372, 16818, 51482, 14760, 55556, 20294, 39429, 21390, 33593, 3633, 12418, 37871, 40793, 56551, 2861, 18026, 43669, 35662, 57848, 33426, 10212, 22871, 25823, 48241, 25359, 14470, 12986, 45502, 32693, 40669, 2759, 8676, 47237, 24426, 12689, 13946, 20120, 25295, 60], 'chosen_samples_score': ['0.92024046', '0.9222082', '0.9233423', '0.9236716', '0.92553264', '1.0007383', '0.9354243', '0.93842345', '0.98422766', '1.0815437', '0.93692404', '1.1199865', '0.9851016', '0.9383716', '0.9948927', '0.958123', '0.99445564', '0.9541118', '0.9962163', '0.93246645', '0.97531337', '0.941601', '0.93612725', '0.9809531', '0.9707747', '0.94834983', '0.9354937', '0.9284797', '0.9665039', '0.9507481', '0.95502985', '0.9791688', '0.97204125', '0.93097967', '1.1463572', '0.93177783', '0.93225884', '0.9379374', '1.0145285', '0.93166244']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8588, 'nll': 0.8097750980377197}, 'chosen_samples': [28373, 57880, 33252, 39593, 33650, 12938, 50303, 11044, 18598, 2381, 16992, 14843, 43176, 29400, 48933, 55141, 13265, 1465, 31744, 2064, 11585, 8301, 3352, 50625, 27130, 41576, 13677, 31301, 10923, 41933, 33383, 11639, 36852, 30559, 18656, 19111, 30203, 50370, 27072, 47068], 'chosen_samples_score': ['0.8476812', '0.84769183', '0.8550261', '0.86099017', '0.85707146', '0.858935', '0.8642794', '0.86110646', '0.8525399', '0.8538707', '0.8657707', '0.85952175', '0.862516', '0.86583203', '0.8700938', '0.86772245', '0.8680476', '0.87308276', '0.8703998', '0.8716401', '0.87824076', '0.90137', '0.9291926', '0.9017915', '0.89131546', '0.9024367', '0.9378963', '0.8903682', '0.88253635', '0.881712', '0.9597762', '0.91812396', '0.89982855', '0.95449835', '0.9172833', '0.88069063', '0.94103426', '0.88273436', '0.88799596', '0.8808776']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.8963, 'nll': 0.6248755090713501}, 'chosen_samples': [59395, 44202, 47475, 40305, 31090, 15832, 39668, 8890, 26034, 31883, 38898, 49809, 56642, 50981, 26444, 11489, 4360, 29672, 2092, 24883, 21102, 35249, 1642, 15869, 15945, 29972, 21461, 18420, 37643, 31941, 28734, 14290, 12416, 59361, 16444, 35498, 48384, 4138, 49692, 3719], 'chosen_samples_score': ['0.89529747', '1.0112202', '0.943405', '1.0493078', '0.9566174', '0.90159476', '0.94929683', '1.0580018', '1.0024519', '0.9096125', '0.91805726', '0.92024475', '0.989342', '0.9265765', '0.9988308', '0.93240076', '0.924939', '0.9005841', '0.9363809', '0.9055466', '0.92643636', '0.9343094', '0.925091', '0.9027512', '0.95673746', '1.0594081', '0.9598824', '0.9750385', '0.924388', '0.9399366', '0.95494974', '0.964109', '0.93081397', '0.9047827', '0.9192946', '0.9579006', '0.90820533', '0.9035087', '0.9587596', '0.9460908']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9265, 'nll': 0.4884255058288574}, 'chosen_samples': [41239, 23463, 40654, 54186, 59314, 22167, 15855, 36750, 22083, 29132, 21674, 50461, 8714, 8447, 55906, 35971, 55513, 57270, 12123, 24457, 19586, 1239, 57882, 16164, 5155, 32573, 14896, 34122, 30658, 59747, 17521, 25158, 31917, 24424, 34445, 13749, 51832, 47768, 30156, 34660], 'chosen_samples_score': ['0.81758535', '0.8179803', '0.81805706', '0.8184979', '0.8189451', '0.819699', '0.819111', '0.82064915', '0.8448553', '0.8263607', '0.84439224', '0.9058031', '0.98685694', '0.95323014', '0.83342826', '0.87182844', '0.82546324', '0.8257561', '0.90964466', '0.9123667', '0.8429289', '0.8520482', '0.8312916', '0.82743394', '0.92642146', '0.88635725', '0.8505158', '0.9143207', '0.86823004', '0.8432155', '0.82868654', '0.8878222', '0.86431944', '0.8870091', '0.8912784', '0.8357788', '0.8685126', '0.92717147', '0.847496', '0.87594074']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9425, 'nll': 0.42390401248931886}, 'chosen_samples': [5308, 16804, 55438, 50912, 57523, 53854, 49149, 35401, 13156, 45073, 109, 41802, 51144, 52169, 44736, 57718, 52012, 14540, 39453, 21174, 56612, 5679, 30844, 13709, 32421, 22272, 5170, 48349, 54994, 23086, 5315, 33222, 31738, 5370, 3030, 20641, 23177, 4153, 35638, 27176], 'chosen_samples_score': ['0.8888325', '0.8906239', '0.8910609', '0.8944265', '0.9020103', '0.9010907', '0.91127753', '0.9076991', '0.9078872', '0.8993805', '0.9137234', '0.9196729', '1.0183704', '0.9296406', '0.9917143', '0.9573663', '0.95291036', '0.9335719', '0.93243515', '1.0888934', '0.93242407', '1.044888', '0.9966143', '0.97363573', '1.0814054', '1.0810473', '0.9294615', '0.9803297', '0.9898925', '0.943224', '1.0021749', '0.9404769', '0.94711655', '0.92985713', '0.97106344', '0.9437129', '0.9241104', '1.0174456', '0.9620857', '0.9499506']})
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store['iterations'].append({'num_epochs': 13, 'test_metrics': {'accuracy': 0.964, 'nll': 0.3162534007072449}, 'chosen_samples': [1674, 14329, 59681, 38526, 19344, 52113, 22139, 13350, 9290, 19814, 16836, 26588, 50433, 18324, 9118, 49890, 24940, 43745, 19507, 47479, 29286, 21601, 18398, 57742, 45954, 55320, 9436, 39355, 7990, 48973, 45069, 26527, 28136, 53496, 49910, 37161, 4355, 20110, 57732, 42415], 'chosen_samples_score': ['0.9438213', '0.9450529', '0.9452308', '0.95691615', '0.9504159', '0.9459499', '0.9518587', '0.96996224', '0.9572909', '0.9719014', '0.9464061', '0.9763261', '0.98549604', '1.055802', '1.0509648', '1.0321729', '1.0601158', '1.008642', '1.0168196', '0.9894265', '1.0389274', '0.98379904', '1.0221281', '1.1492186', '1.1808367', '0.99459296', '1.0021741', '1.1687052', '0.9871999', '0.9948129', '1.066016', '1.0531827', '1.0232875', '1.0486445', '1.0174885', '0.98643947', '0.98963094', '0.9929395', '0.992242', '0.97785306']})
store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9633, 'nll': 0.29741691637039186}, 'chosen_samples': [8532, 32814, 26756, 13191, 52319, 37048, 19188, 13388, 41094, 50317, 935, 43811, 37838, 8940, 27596, 13370, 32323, 42973, 32668, 53019, 36072, 53844, 58832, 54950, 22561, 10884, 10044, 7589, 4185, 31954, 16011, 32047, 20050, 52014, 2714, 12196, 32276, 15276, 49121, 31545], 'chosen_samples_score': ['0.8641295', '0.8643233', '0.8644314', '0.8646244', '0.87178826', '0.87345093', '0.8720025', '0.8752138', '0.87632906', '0.87677246', '0.8775645', '0.8954079', '0.9934515', '0.90757716', '0.90777117', '0.94494045', '0.9643738', '0.91181684', '0.9184302', '0.9602641', '1.1443621', '1.0446246', '0.95811135', '0.90577966', '0.90427476', '0.8832164', '0.89099586', '0.9049228', '0.91929656', '0.9042128', '0.88313586', '0.90040255', '0.9762816', '0.9238169', '0.88594806', '1.0288188', '0.9084487', '0.95398176', '0.8873444', '0.9042954']})
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store['iterations'].append({'num_epochs': 10, 'test_metrics': {'accuracy': 0.9692, 'nll': 0.2893406298160553}, 'chosen_samples': [47741, 27458, 3691, 34520, 31283, 48726, 47870, 28930, 58560, 46832, 33812, 5630, 32776, 16572, 21204, 29311, 26778, 49153, 51764, 38408, 31753, 59924, 9677, 16768, 49576, 4799, 17814, 17741, 47599, 27411, 23486, 3070, 44172, 31184, 16756, 28392, 41478, 27358, 32108, 29320], 'chosen_samples_score': ['0.83936065', '0.83998346', '0.84004366', '0.840489', '0.84068555', '0.8418344', '0.8427051', '0.8434072', '0.84435743', '1.0399754', '0.88761985', '0.89037514', '0.91679674', '0.87392324', '0.8663739', '0.86896664', '0.8561411', '0.9237416', '0.93824756', '0.9554643', '0.86740446', '0.9116673', '0.84904', '0.85145986', '0.8923443', '0.86241746', '0.8540117', '1.0460887', '0.8977256', '0.9010808', '0.88450104', '0.8937431', '0.89741206', '0.85822505', '0.8786722', '0.8914938', '0.8607234', '0.93102354', '0.8794466', '0.96355325']})
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